How to load own data set into neural network?

Hi,

I am fairly new to MATLAB and I would like help in understanding about datasets. For classification in neural network, the example for wine classification show:

[x,t] = wine_dataset;

size(x)

size(t)

net = patternnet(10);

view(net)

I have a dataset of input [8x4]matrix and target [4x4]matrix. How do I input this into neural network such that I can use the function patternnet?

Thanks in advance for your help.

Answer 1

[Greg Heath]

Exactly as indicated in

help patternnet

and

doc patternnet

where

x = yourinput;

t = yourtarget; % target columns should be unit vectors with a single 1

DOCUMENTATION BUG: the default input is (10,'trainscg') NOT (20,'trainlm')!

close all, clear all, clc

 [ x, t ] = iris_dataset;
 [ I N ]  = size(x)         % [ 4 150 ]
 [ O N ]  = size(t)         % [ 3 150 ]
 trueclass  = vec2ind(t);
 MSE00  = mean(var(t',1)) % 0.222 biased MSE for naive constant output model
 MSE00a = mean(var(t',0)) % 0.224 unbiased MSE for naive constant output model
 Ntrn    = N - 2*(0.15*N) % 105 default size of training set
 Ntrneq  = Ntrn*O         % 315 Number of training equations

% Nw = (I+1)*H+(H+1)*O % No. of unknown weights for H hidden nodes

 Hub = -1 + ceil( (Ntrneq-O)/(I+O+1))% 38 Upperbound for H if Ntrneq > Nw
 H   = floor(Hub/10)% 3 Desire Ntrneq >> Nw to mitigate noise and errors in data
 Nw   = (I+1)*H+(H+1)*O %  27 Number of unknown weights
 Ndof = Ntrneq - Nw % 288 No. of estimation degrees of freedom
(See Wikipedia reference below)
 MSEgoal = 0.01*Ndof*MSE00a/Ntrneq % 0.002 Desire adjusted R^2 >= 0.99

http://en.wikipedia.org/wiki/Adjusted_R-squared

 MinGrad = MSEgoal/10             %  2.05e-4

 rng(0)                           % Initialize RNG
 net = patternnet(H);
 net.trainParam.goal     = MSEgoal;
 net.trainParam.min_grad = MinGrad;
 [net tr Y E ] = train(net,x,t);   % E = t-Y

 stopcrit = tr.stop             % Validation stop.
 numepochs = tr.num_epochs      % 60
 bestepoch = tr.best_epoch      % 54
 MSE = tr.perf(bestepoch+1)     % 0.0115
 MSEa = Ntrneq*MSE/Ndof         % 0.0126
 R2 = 1-MSE/MSE00               % 0.948
 R2a = 1-MSEa/MSE00a            % 0.944
 assignedclass = vec2ind(Y);
 err = assignedclass~=trueclass;
 Nerr = sum(err)                % 2
 PctErr = 100*Nerr/N            % 1.33

result = [ H numepochs bestepoch R2 R2a Nerr PctErr]

result = [ 3 60 54 0.948 0.944 2 1.33 ]

% NOTE1: Can use tr to obtain trn/val/tst breakdown

%NOTE2: To imprcve performance try other (e.g.,9 more) random weight initializations. If that doesn't work try 10 random weight initialization trials with H = 4.

Hope this helps.

Thank you for formally accepting my answer

Greg

[Shoumy]

Thanks so much Greg for your long and detailed answer. Really appreciate it. So, to reconfirm the method, I have to enter/load x and t before using datasets?

x = load ('Bloodratio.mat');

t = load ('BloodratioTarget.mat');

[ x, t ] = iris_dataset;

 [ I N ]  = size(x)       
 [ O N ]  = size(t)

Like this? Sorry for asking such simple questions..I really am a newbie. Thank you for bearing with me. :)

[Greg Heath]

Delete the iris_dataset line. That nndata was just used for demonstration purposes.

help nndatasets

[Shoumy]

Thanks, Greg. Really helpful. :)

[sarath chandrika balaji]

what is n in the code?

Answer 2

[Greg Heath]

Whoops! I made a mistake. If you type the command

tr = tr

you will see that tr.perf, tr.vperf and tr.tperf are the individual MSEs for training, validation and testing. The total MSE is obtained from

MSE = mse(E)

Sorry for the bum steer.

Greg

[Shoumy]

Ok. No problem. :) Thanks, Greg.

Answer 3

[primrose khaleed]

Hi ...I am new in neural network ...I have folder which store in it images after processing it ...I want to enter this image into neural network...please how can do it...how can consist target and trinning matrix....please help me....